Biometric forms of security have become increasingly popular. For example, a biometric form of security may provide convenient access to an electronic device without a password, for example, and, more particularly, without having to type the password, which is often time consuming. Some biometric security techniques include using a fingerprint, an iris of a user's eye, and facial recognition techniques for identification and authentication.
Of the biometric forms of security, facial recognition has become relatively mainstream in recent years, as an increased amount of applications for photo tagging and security has become available to consumers. Improvements in algorithm accuracy, computing capacity, and the growing availability of front-facing cameras on desktop computers, laptop computers, tablet computers, and mobile phones contribute to relatively rapid advancements in capability and availability of these approaches.
Interest in facial recognition may also be attributed to the fact that humans may readily associate with the concept of facial recognition. For example, a person may use the concept of facial recognition several hundred if not several thousand times each day. The facial recognition process is generally a subconscious one, but it is one that may be highly valued in social and business interactions, for example.
Computer automation of facial recognition started in the 1960's. These early approaches generally required a manual step to register key points on the user's face. These points would include the corners of the mouth, eyes, or nose, for example. Images were captured in controlled lighting conditions and poses would be front-facing. In a very basic sense, the relative position of these various facial features along with head shape may be used to create a mathematical template which is then stored and used for matching.
A typical application for facial recognition is automated identification of mug shot images. Mug shots collected in police departments, for example, are generally available in a database which might be searched automatically to help identify repeat offenders, for example.
Over the past 10 years, however, facial recognition approaches have become more automated to the point that multiple faces may be identified in relatively large crowds. In addition to improvements in functionality, these systems are also becoming more accurate. For example, performance of facial recognition, measured by false accept rates (FAR) has greatly improved, for example, by a factor of two every two years. Despite this ongoing improvement, false acceptance rates for facial recognition remains at 1:1000 versus using a fingerprint, which readily achieves 1:100,000 and can be shown to achieve better than 1:1,000,000 in certain implementations.
There are several approaches for facial recognition. One such approach is two dimensional (2D) facial recognition. In 2D face recognition, a facial recognition system operates on a simple 2D image. These systems tend to be very strongly affected by variations in lighting conditions and pose. A popular method used for 2D facial recognition is principle component analysis (PCA). A benefit of the PCA method is that the resulting templates are quite small. A typical template may only require about 1-2 kilobytes of storage, but some systems may require as much as 75 kilobytes.
Moreover, variations in lighting conditions and face orientation may have significant impact on these systems. For example, glare from glasses or eyes obscured by sunglasses may also degrade performance substantially. These factors may be annoying for indoor use, but may be an extreme problem for an electronic device, for example, a mobile wireless electronic device, which may be subject to these conditions and more, for example, sunlight.
Face position variations may also be an issue when the electronic device is mounted in a dock when used for in-car navigation, for example. Face position may also be an issue in pedestrian situations wherein holding the device up in front of the face may be both awkward and a safety concern.
To address some of these shortcomings, some systems use an infrared light source which enables operation in darkness without obtrusive visible lighting. A combination of both visible light and infrared has been shown to result in improved overall performance.
In 3D face recognition, a model of the facial structure may be produced which may be relatively accurate. From this model, a mathematical template may be created, which is then used for matching.
For 3D imaging, either stereo cameras are used or a geometric pattern is projected onto the user's face. A conventional camera can capture an image of a user's face with this pattern present, and the distortions in the pattern may be used to create the 3D model. The pattern might be produced by a laser. If the pattern is created with an infrared source, it would not be visible to the human eye. One challenge is that the imaging approaches may be relatively large with respect to size in an electronic device (using optics), and a somewhat controlled position (distance) from the user's face may be highly desired.
U.S. Pat. No. 7,957,762 to Herz et al. discloses using an ambient light sensor to augment a proximity sensor output. More particularly, Herz et al. discloses an IR emitter and an IR detector coupled to a microcontroller. The microcontroller may control switching between a proximity sensing mode and ambient light sensing mode by either closing and opening an optional shutter or by turning on and off the power to the IR emitter.
U.S. Patent Application Publication No. 2009/0124376 to Kelly et al. discloses a networked gaming system including anonymous biometric identification. More particularly, Kelly et al. discloses illuminating IR light emitting diodes (LEDs) to illuminate a patron's face upon determining a patron has moved within a perimeter of the system. A sonic signal, a pressure sensor, and a laser may be used to determine whether the patron is within the perimeter. A camera captures an image and transmits the image to a control board for further processing, for example, for facial recognition.
Despite the advances of facial recognition, further improvements are needed. For example, it may be desirable to provide increased accuracy facial recognition, for example, in a mobile wireless communications device, which has a relatively small area for components.